Big Data Analytics Methods for Collision and Grounding Risk Analysis in Real Conditions: Framework, Evaluation, and Applications

Mingyang Zhang

Big Data Analytics Methods for Collision and Grounding Risk Analysis in Real Conditions: Framework, Evaluation, and Applications

Julkaisun otsikon käännös: Big Data -tutkimusmenetelmät törmäys- ja karilleajoriskianalyysiin todellisissa olosuhteissa: puitteet, arviointi ja sovellukset

Mingyang Zhang

Energia- ja konetekniikan laitos

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Laivojen yhteentörmäykset ja karilleajot ovat yleisimmät merionnettomuudet. Ne johtavat usein vaurioihin, jotka aiheuttavat laivaan vuotoja, jonka seurauksena laiva voi kaatua johtaen ihmishenkien menetyksiin tai öljyvuotoihin. Yhteentörmäys- ja pohjakosketusriskit voidaan arvioida laadullisesti asiantuntija-arviolla tai kvantitatiivisesti meriliikennetietojen analysoinnin avulla. Tästä huolimatta datalähtöiset tutkimukset, jotka kuvaavat todellisia toimintaolosuhteita suuria tietomääriä hyödyntäen, ovat edelleen rajallisia. Tässä työssä on kehitetty suureen tietomäärään (ns Big Data) perustuvia tutkimusmenetelmiä törmäys- ja karilleajoriskien arvioimiseen todellisissa käyttöolosuhteissa. Laiva on mallinnettu jäykän kappaleen dynaamisena systeeminä, johon vaikuttaa aallokon aiheuttamat liikkeet, muiden alusten liikennevirrat ja ympäristöolosuhteet. Kehitetty menetelmä perustuu tietopohjaiseen viitekehykseen, jonka tavoitteena on tukea ennakoivaa riskienhallintakäytäntöä, älykästä liikenteen seurantaa ja siihen liittyvien riskienhallintavaihtoehtojen käyttöönottoa olemassa olevien ja tulevaisuuden viranomaisvaatimusten mukaisesti. Menetelmissä hyödynnetään automaattisen tunnistusjärjestelmän (AIS) suuria datavirtoja sekä reaaliaikaista hydrometeorologista (kuten tuulet, aallokko, virtaukset jne.) tietoa ja syvyysdataa. Tulokset validoidaan tutkimalla Suomenlahdella (GoF) liikennöivien risteilyalusten mahdollisia yhteentörmäyksiä ja karilleajoja avoveden aikana. Esitellyt ideat voivat avustaa: (1) tunnistamaan kriittisiä yhteentörmäys- ja karilleajotilanteita, joita ei tällä hetkellä oteta huomioon olemassa olevassa onnettomuustietokannassa, (2) idealisoimaan riskitilanteissa olevia laivoja todellisissa olosuhteissa, (3) määrittelemään uusien yhteentörmäys- ja karilleajoriskien kriteerejä tapauskohtaisesti uusien suorituskykyyn perustuvien standardien kehittämiseksi, ja (4) ennustamaan laivan aikariippuvaista liikerataa riskitilanteiden tunnistamiseksi etukäteen ja riskien vähentämiseksi jo ennakolta. Isojen tietovirtojen todennäköisyyspohjainen toteutus on todettu hyödylliseksi törmäys- ja pohjakosketusriskien ennakoivassa arvioinnissa todellisissa käyttöolosuhteissa, ja koneoppimismenetelmät (ML) voivat parantaa ymmärrystämme alusten liikeratojen vaikutuksesta toimintariskiin. Johtopäätöksenä todetaan, että keskipitkällä ja pitkällä aikavälillä ehdotetut lähestymistavat voivat auttaa kehittämään merenkulun riskinhallintatyökaluja ja älykkäitä päätöksenteon tukijärjestelmiä käytössä oleville aluksille. Tällaiset menetelmät voivat auttaa parantamaan alusten turvallisuusstandardeja ja operointi periaatteita.

Julkaisun otsikon käännös	Big Data -tutkimusmenetelmät törmäys- ja karilleajoriskianalyysiin todellisissa olosuhteissa: puitteet, arviointi ja sovellukset
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Hirdaris, Spyros, Vastuuprofessori Kujala, Pentti, Ohjaaja
Kustantaja	Aalto University
Painoksen ISBN	978-952-64-1066-1
Sähköinen ISBN	978-952-64-1067-8
Tila	Julkaistu - 2022
OKM-julkaisutyyppi	G5 Artikkeliväitöskirja

Tutkimusalat

laivojen turvallisuus
suuri tietomäärä (Big Data) tutkimus
koneoppimismenetelmät
törmäys- ja karilleajoriskit
hydrometeorologiset olosuhteet
laivan dynamiikka

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-64-1067-8

Siteeraa tätä

@phdthesis{866b8cab5868411f8957194e7788e59c,

title = "Big Data Analytics Methods for Collision and Grounding Risk Analysis in Real Conditions: Framework, Evaluation, and Applications",

abstract = "Collisions and groundings are the most frequent maritime accidents. They often lead to damages resulting in ship flooding and subsequent capsizing, loss of human life or oil spills. Collision and grounding risks can be evaluated qualitatively by expert judgment or quantitatively through the analysis of maritime traffic data. Yet, data-driven studies reflecting real operational conditions using big data remain limited. This research proposes big data analytics methods for the evaluation of collision and grounding risk in real operational conditions. The ship is modelled as a rigid body dynamic system subject to wave-induced motions, traffic flows, and environmental conditions. The notion developed introduces a data-driven framework that aims to support proactive risk management practice, intelligent monitoring and the implementation of associated risk control options in accordance with existing and emerging regulatory requirements. The methods make use of big data streams from the Automatic Identification System (AIS) as well as nowcast hydrometeorological (e.g., wind, wave, current, etc.) and bathymetry data. Results are validated by studying potential collisions and groundings of cruise ships operating in the Gulf of Finland (GoF) during ice-free periods. It is demonstrated that the ideas introduced can assist with (1) the identification of critical collision and grounding scenarios that are not currently accounted for by existing accident databases, (2) the idealization of the fleet at risk in real conditions, (3) the definition of novel collision and grounding risk criteria for ad hoc use within the context of emerging performance-based standards, and (4) the prediction of time-varying ship motion trajectories for recognizing risky situations in advance and proactive risk mitigation. It is confirmed that the probabilistic implementation of big data streams is useful for the proactive evaluation of collision and grounding risks in real operational conditions and Machine Learning (ML) methods can increase a priory our insight into the influence of ship motion trajectories on operational risk. It is therefore concluded that over the medium to long term, the proposed approaches could help develop maritime risk management tools and intelligent decision support systems for ongoing ships in operation. Such methods can help improve ship safety standards and operational practices.",

keywords = "ship safety, big data analytics, machine Learning methods, collision and grounding risks, hydrometeorological conditions, ship dynamics, laivojen turvallisuus, suuri tietom{\"a}{\"a}r{\"a} (Big Data) tutkimus, koneoppimismenetelm{\"a}t, t{\"o}rm{\"a}ys- ja karilleajoriskit, hydrometeorologiset olosuhteet, laivan dynamiikka, ship safety, big data analytics, machine Learning methods, collision and grounding risks, hydrometeorological conditions, ship dynamics",

author = "Mingyang Zhang",

year = "2022",

language = "English",

isbn = "978-952-64-1066-1",

series = "Aalto University publication series DOCTORAL THESES",

publisher = "Aalto University",

number = "191",

address = "Finland",

school = "Aalto University",

}

TY - BOOK

T1 - Big Data Analytics Methods for Collision and Grounding Risk Analysis in Real Conditions: Framework, Evaluation, and Applications

AU - Zhang, Mingyang

PY - 2022

Y1 - 2022

N2 - Collisions and groundings are the most frequent maritime accidents. They often lead to damages resulting in ship flooding and subsequent capsizing, loss of human life or oil spills. Collision and grounding risks can be evaluated qualitatively by expert judgment or quantitatively through the analysis of maritime traffic data. Yet, data-driven studies reflecting real operational conditions using big data remain limited. This research proposes big data analytics methods for the evaluation of collision and grounding risk in real operational conditions. The ship is modelled as a rigid body dynamic system subject to wave-induced motions, traffic flows, and environmental conditions. The notion developed introduces a data-driven framework that aims to support proactive risk management practice, intelligent monitoring and the implementation of associated risk control options in accordance with existing and emerging regulatory requirements. The methods make use of big data streams from the Automatic Identification System (AIS) as well as nowcast hydrometeorological (e.g., wind, wave, current, etc.) and bathymetry data. Results are validated by studying potential collisions and groundings of cruise ships operating in the Gulf of Finland (GoF) during ice-free periods. It is demonstrated that the ideas introduced can assist with (1) the identification of critical collision and grounding scenarios that are not currently accounted for by existing accident databases, (2) the idealization of the fleet at risk in real conditions, (3) the definition of novel collision and grounding risk criteria for ad hoc use within the context of emerging performance-based standards, and (4) the prediction of time-varying ship motion trajectories for recognizing risky situations in advance and proactive risk mitigation. It is confirmed that the probabilistic implementation of big data streams is useful for the proactive evaluation of collision and grounding risks in real operational conditions and Machine Learning (ML) methods can increase a priory our insight into the influence of ship motion trajectories on operational risk. It is therefore concluded that over the medium to long term, the proposed approaches could help develop maritime risk management tools and intelligent decision support systems for ongoing ships in operation. Such methods can help improve ship safety standards and operational practices.

AB - Collisions and groundings are the most frequent maritime accidents. They often lead to damages resulting in ship flooding and subsequent capsizing, loss of human life or oil spills. Collision and grounding risks can be evaluated qualitatively by expert judgment or quantitatively through the analysis of maritime traffic data. Yet, data-driven studies reflecting real operational conditions using big data remain limited. This research proposes big data analytics methods for the evaluation of collision and grounding risk in real operational conditions. The ship is modelled as a rigid body dynamic system subject to wave-induced motions, traffic flows, and environmental conditions. The notion developed introduces a data-driven framework that aims to support proactive risk management practice, intelligent monitoring and the implementation of associated risk control options in accordance with existing and emerging regulatory requirements. The methods make use of big data streams from the Automatic Identification System (AIS) as well as nowcast hydrometeorological (e.g., wind, wave, current, etc.) and bathymetry data. Results are validated by studying potential collisions and groundings of cruise ships operating in the Gulf of Finland (GoF) during ice-free periods. It is demonstrated that the ideas introduced can assist with (1) the identification of critical collision and grounding scenarios that are not currently accounted for by existing accident databases, (2) the idealization of the fleet at risk in real conditions, (3) the definition of novel collision and grounding risk criteria for ad hoc use within the context of emerging performance-based standards, and (4) the prediction of time-varying ship motion trajectories for recognizing risky situations in advance and proactive risk mitigation. It is confirmed that the probabilistic implementation of big data streams is useful for the proactive evaluation of collision and grounding risks in real operational conditions and Machine Learning (ML) methods can increase a priory our insight into the influence of ship motion trajectories on operational risk. It is therefore concluded that over the medium to long term, the proposed approaches could help develop maritime risk management tools and intelligent decision support systems for ongoing ships in operation. Such methods can help improve ship safety standards and operational practices.

KW - ship safety

KW - big data analytics

KW - machine Learning methods

KW - collision and grounding risks

KW - hydrometeorological conditions

KW - ship dynamics

KW - laivojen turvallisuus

KW - suuri tietomäärä (Big Data) tutkimus

KW - koneoppimismenetelmät

KW - törmäys- ja karilleajoriskit

KW - hydrometeorologiset olosuhteet

KW - laivan dynamiikka

KW - ship safety

KW - big data analytics

KW - machine Learning methods

KW - collision and grounding risks

KW - hydrometeorological conditions

KW - ship dynamics

M3 - Doctoral Thesis

SN - 978-952-64-1066-1

T3 - Aalto University publication series DOCTORAL THESES

PB - Aalto University

ER -

Big Data Analytics Methods for Collision and Grounding Risk Analysis in Real Conditions: Framework, Evaluation, and Applications

Abstrakti

Tutkimusalat

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